Most of Gemini's users are Search converts doing extended-Search-like behaviors.
Agentic workflows are a VERY small percentage of all LLM usage at the moment. As that market becomes more important, Google will pour more resources into it.
> Agentic workflows are a VERY small percentage of all LLM usage at the moment. As that market becomes more important, Google will pour more resources into it.
I do wonder what percentage of revenue they are. I expect it's very outsized relative to usage (e.g. approximately nobody who is receiving them is paying for those summaries at the top of search results)
> Most agent actions on our public API are low-risk and reversible. Software engineering accounted for nearly 50% of agentic activity, but we saw emerging usage in healthcare, finance, and cybersecurity.
this doesn’t answer your question, but maybe Google is comfortable with driving traffic and dependency through their platform until they can do something like this
In mid-2024, Anthropic made the deliberate decision to stop chasing benchmarks and focus on practical value. There was a lot of skepticism at the time, but it's proven to be a prescient decision.
Benchmarks are basically straight up meaningless at this point in my experience. If they mattered and were the whole story, those Chinese open models would be stomping the competition right now. Instead they're merely decent when you use them in anger for real work.
I'll withhold judgement until I've tried to use it.
Ranking Codex 5.2 ahead of plain 5.2 doesn't make sense. Codex is expressly designed for coding tasks. Not systems design, not problem analysis, and definitely not banking, but actually solving specific programming tasks (and it's very, very good at this). GPT 5.2 (non-codex) is better in every other way.
Codex has been post-trained for coding, including agentic coding tasks.
It's certainly not impossible that the better long-horizon agentic performance in Codex overcomes any deficiencies in outright banking knowledge that Codex 5.2 has vs plain 5.2.
Let's give it a couple of days since no one believes anything from benchmarks, especially from the Gemini team (or Meta).
If we see on HN that people are willing switching their coding environment, we'll know "hot damn they cooked" otherwise this is another wiff by Google.
It's like anything Google - they do the cool part and then lose interest with the last 10%. Writing code is easy, building products that print money is hard.
That monopoly is worth less as time goes by and people more and more use LLMs or similar systems to search for info. In my case I've cut down a lot of Googling since more competent LLMs appeared.
Accomplish the task I give to it without fighting me with it.
I think this is classic precision/recall issue: the model needs to stay on task, but also infer what user might want but not explicitly stated. Gemini seems particularly bad that recall, where it goes out of bounds
OpenAI has mostly caught up with Claude in agentic stuff, but Google needs to be there and be there quickly